Schedule: 增强学习（Reinforcement Learning） sessions

One of the areas we’re most interested in is the emerging applications of reinforcement learning (RL). We’ve all read about the key role RL played in systems that learned how to exceed human players in computer games and classic board games. But can RL be used in practical, real-world applications? As always it’s good to start out with disclaimers: RL requires a lot of data and simulations, and research results tend to be difficult to reproduce.

However, two things seem point towards the direction of RL applications. First, tools for writing RL models and plugging them into simulators are starting to emerge, and many of them target developers who aren’t experts in machine learning. Secondly, companies are very interested in automation, particularly low-skilled tasks that occupy high-skilled workers. In this context automation is sometimes referred to as robotics process automation or enterprise workflow automation. Many tasks that involve sequential decision making are amenable to learning/training making them ideal candidates for RL based automation solutions. The democratization of tools coupled with the interest in automation (using learning rather than programming and rules), points towards interesting applications of RL in the near future.

We will feature keynotes, talks, and tutorials that will introduce the latest RL tools as well as applications to industrial automation and manufacturing, autonomous vehicles, and software development.

Recently, computers have been able to learn to play Atari games, Go, and first-person shooters at a superhuman level. Underlying all these accomplishments is deep reinforcement learning. Arthur Juliani offers a deep dive into reinforcement learning, from the basics using lookup tables and GridWorld all the way to solving complex 3D tasks with deep neural networks.
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Danny Lange offers an overview of deep reinforcement learning, an exciting new chapter in AI’s history that is changing the way we develop and test learning algorithms that can later be used in real life.
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Danny Lange demonstrates the role games can play in driving the development of reinforcement learning algorithms. Danny uses the Unity Engine with the ML-Agents toolkit as an example of how dynamic 3D game environments can be utilized for machine learning research.
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Mark Hammond explores a wide breadth of real-world applications of deep reinforcement learning, including robotics, manufacturing, energy, and supply chain. Mark also shares best practices and tips for building and deploying these systems, highlighting the unique requirements and challenges of industrial AI applications.
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